Submission Date

4-26-2019

Document Type

Paper- Restricted to Campus Access

Department

Neuroscience

Second Department

Psychology

Adviser

Jennifer Stevenson

Second Adviser

Joel Bish

Committee Member

Robin Clouser

Committee Member

Susan Adam

Department Chair

Ellen Dawley

Department Chair

Brent Mattingly

Project Description

Explicit evaluations of testing present limitations, such as cramming, and alternate form reliability, leading researchers to question, is there a better way to evaluate student knowledge? The Structural Assessment of Knowledge (SAK) is a form of implicit testing that examines organization of knowledge structures and may help to eliminate some issues seen with explicit testing. This study investigates how undergraduate students (n=68) majoring in neuroscience and psychology compare on implicit and explicit evaluations of brain structures and function, statistics, and neuroscience techniques. Students completed pairwise rankings of 15-16 words for each of the three domains of interest (statistics, brain structures and functions, and neuroscience techniques). Pathfinder software created concept maps from student rankings, which were compared to a network of neuroscience professors well versed in these topics. Three expert groups were constructed: two neuroscience professors at Ursinus who teach the domains assessed, five neuroscience professors at Ursinus who do not necessarily teach the domains assessed, and neuroscience professors from various colleges. Three 15-question multiple choice tests were used to evaluate explicit knowledge for each topic. The network similarity for SAK was compared to total accuracy on multiple-choice tests. Analysis revealed, students preformed best on domains which were heavily emphasized in curriculum. Neuroscience students performed better on assessments of brain structures and functions and neuroscience techniques, but students performed equally on statistics. Overall results from the SAK mirrored results of multiple-choice accuracy which may indicate that implicit tests of knowledge may work as a future form of alternative testing.

Comments

This project benefited from the Winnifred Cutler Fund.

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